6D Object Pose Estimation. Given the 3D models of objects, the goal of 6D pose estimation is to estimate the 3D translation and the 3D rotation of these objects from input images. Traditionally, the problem is tackled by local feature matching [1]}, [2]}, [3]} or template matching [4]}, [5]}, [6]}. Recently, learning-based methods receive more attention due to their ability to generalize to different backgrounds, lighting, and occlusions [7]}, [8]}, [9]}. Deep neural networks further boost the performance by learning more discriminative features and classifiers [10]}, [11]}, [12]}, [13]}, [14]}. However, deep learning methods also require more data for training.
